




'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase_					=   {
    "configuration_clap": [
        "CLAP_PRETRAINED_MODEL_ARCHIVE_LIST",
        "ClapAudioConfig",
        "ClapConfig",
        "ClapTextConfig",
    ],
    "processing_clap": ["ClapProcessor"],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_					=   [
      "CLAP_PRETRAINED_MODEL_ARCHIVE_LIST",
      "ClapModel",
      "ClapPreTrainedModel",
      "ClapTextModel",
      "ClapTextModelWithProjection",
      "ClapAudioModel",
      "ClapAudioModelWithProjection",
  ]
  UpperCAmelCase_					=   ["ClapFeatureExtractor"]

if TYPE_CHECKING:
  from .configuration_clap import (
      CLAP_PRETRAINED_MODEL_ARCHIVE_LIST,
      ClapAudioConfig,
      ClapConfig,
      ClapTextConfig,
  )
  from .processing_clap import ClapProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .feature_extraction_clap import ClapFeatureExtractor
    from .modeling_clap import (
        CLAP_PRETRAINED_MODEL_ARCHIVE_LIST,
        ClapAudioModel,
        ClapAudioModelWithProjection,
        ClapModel,
        ClapPreTrainedModel,
        ClapTextModel,
        ClapTextModelWithProjection,
    )


else:
  import sys

  UpperCAmelCase_					=   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)









'''simple docstring'''


from __future__ import annotations

import csv

import requests
from bsa import BeautifulSoup


def     SCREAMING_SNAKE_CASE							(   a_						:				str = ""      ):
  __a								=       url or 'https://www.imdb.com/chart/top/?ref_=nv_mv_250'
  __a								=       BeautifulSoup(requests.get(a_      ).text					,       'html.parser'      )
  __a								=       soup.find_all('td'					,       attrs='titleColumn'      )
  __a								=       soup.find_all('td'					,       class_='ratingColumn imdbRating'      )
  return {
      title.a.text: float(rating.strong.text      )
      for title, rating in zip(a_					,       a_      )
  }


def     SCREAMING_SNAKE_CASE							(   a_						:				str = "IMDb_Top_250_Movies.csv"      ):
  __a								=       get_imdb_top_aaa_movies()
  with open(a_					,       'w'					,       newline=''      ) as out_file:
    __a								=       csv.writer(a_      )
    writer.writerow(['Movie title', 'IMDb rating']      )
    for title, rating in movies.items():
      writer.writerow([title, rating]      )


if __name__ == "__main__":
  write_movies()



